Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators, and role in key physiological response

Abstract

<jats:p>The ability of <jats:italic>Staphylococcus aureus</jats:italic> to infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from two <jats:italic>S. aureus</jats:italic> clinical strains (TCH1516 and LAC). ICA decomposed the <jats:italic>S. aureus</jats:italic> transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in <jats:italic>S. aureus</jats:italic>, and a platform enabling its full elucidation.</jats:p>

Identifying the effect of vancomycin on health care–associated methicillin-resistant Staphylococcus aureus strains using bacteriological and physiological media

“…The increasingly ubiquitous RNA-seq data have been used to predict the transcriptional regulatory networks in S. aureus , where antibodies for most regulators are not readily available [ 36 ]. These data have also been used to predict fitness and sensitivities to different antibiotics in different pathogens [ 37 ].…”

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

“…Escherichia coli PRECISE ( 21 ) and Staph PRECISE ( 22 ) were generated using RNA sequencing (RNA-Seq), as described in their respective publications. The Bacillus subtilis dataset ( 23 ) is a well-known microarray dataset originally published by Nicolas et al.…”

“…We recently applied ICA to high quality transcriptomic datasets for three species of bacteria: Escherichia coli ( 21 ), Staphylococcus aureus ( 22 ) and Bacillus subtilis ( 23 ). In our work with E. coli , we termed the independent signals ‘iModulons’, for independently modulated gene sets.…”

“…We then assigned categories, functions, and regulators to each iModulon. The regulator assignments were based on existing knowledge of transcription factor binding sites ( 3–5 , 22 ), or, in some cases, a boolean combination of several regulons. By comparing our decompositions to known regulons described in other databases, we were able to identify potentially mislabeled or previously unknown TF-gene associations and verify them with ChIP-exo binding profiles.…”

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

“…Reconstructing GEMs for multiple strains across a single species has enabled a systems-level approach to study and characterize the pan-metabolic capabilities of the species. Pangenomic studies have been accomplished for a wide range of species from Staphylococcus aureus to Klebsiella pneumoniae [ 58 , 59 , 60 , 61 , 62 , 63 ]. Integration of genomics, phenomics, transcriptomics, and genome-scale modeling for seven commonly used E. coli strains linked molecular features to strain-specific phenotypes.…”